Shift-Invariant Adaptive Double Threading: Learning MHC II–Peptide Binding

Abstract

The major histocompatibility complex (MHC) plays important roles in the workings of the human immune system. Specificity of MHC binding to peptide fragments from cellular and pathogens' proteins has been found to correlate with disease outcome and pathogen or cancer evolution. In this paper we propose a novel approach to predicting binding configurations and energies for MHC class II molecules, whose epitopes are generally predicted less well than the MHC I epitopes due in part to larger variation in bound peptide length. We treat the relative position of the peptide as a hidden variable, and model the ensemble of different binding configurations, rather than use a separate alignment procedure to narrow it down to one. Thus, our predictor infers a distribution over peptide positions from the MHC II and peptide sequences, and computes the total binding affinity. The training procedure iterates the predictions with re-estimation of the parameters of the binding groove model. For a given relative peptide position, any MHC class I prediction model can be used. Here we choose the physics based model of Jojic et al. (2006). We show that the parameters of the binding model can be learned efficiently from the training data and then used to estimate binding energies for previously untested peptides. Our technique performs on par with previous approaches to MHC II epitope prediction. Furthermore, our model choice allows generalization to new MHC class II alleles, which were not a part of the training set.

NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ

“…Using thousands of peptide-binding data points, several predictors have been developed and benchmarked (for review, see Nielsen et al 2010b). One very important subset of these predictors consists of the so-called pan-specific methods that are capable of obtaining accurate predictions for molecules with limited or no binding data (Nielsen et al 2008, 2010a; Zaitlen et al 2008; Zhang et al 2005). For MHC class I prediction, it has been demonstrated that a pan-specific approach can benefit from being trained on cross-loci, and even cross-species, data.…”

TEPITOPEpan: Extending TEPITOPE for Peptide Binding Prediction Covering over 700 HLA-DR Molecules

“…These PSSMs are derived from 11 HLA DRB alleles: DRB1*01:01, DRB1*03:01, DRB1*04:01, DRB1*04:02, DRB1*04:04, DRB*07:01, DRB1*08:01, DRB1*11:01, DRB1*13:02, DRB1*15:01 and DRB5*01:01 (the alleles in this paper are represented in current HLA allele nomenclature [24]). There are other five pan-specific methods: MHCIIMulti [25], NetMHCIIpan-1.0 [26], NetMHCIIpan-2.0 [27], MultiRTA [28] and SIADT (Shift Invariant Adaptive Double Threading) [29]. MHCIIMulti is a kernel based method, in which the binding specificity of a target MHC with no binding data can be predicted by using binding data of related MHC alleles.…”

Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules

“…Evaluation of those methods was typically done using existing structures or a small dataset of known binders and none of them currently provides a public web server. Finally, so-called pan-specific MHC binding predictors have been developed in recent years integrating structural information with experimental peptide binding data allowing for generalization of binding predictions to MHC molecules characterized with few or even no peptide binding data [12], [13], [14], [15], [16], [17] [18].…”